Using Whole-Genome Sequence Data to Predict Quantitative Trait Phenotypes in Drosophila melanogaster

Daniel Gianola,Henner Simianer,Julien F Ayroles,Martin Schlather,Stephen Richards,Eric A Stone,Dianhui Zhu,Christian Stricker,Ulrike Ober,Trudy F C Mackay,Richard A Gibbs

doi:10.1371/journal.pgen.1002685

Abstract

Predicting organismal phenotypes from genotype data is important for plant and animal breeding, medicine, and evolutionary biology. Genomic-based phenotype prediction has been applied for single-nucleotide polymorphism (SNP) genotyping platforms, but not using complete genome sequences. Here, we report genomic prediction for starvation stress resistance and startle response in Drosophila melanogaster, using ∼2.5 million SNPs determined by sequencing the Drosophila Genetic Reference Panel population of inbred lines. We constructed a genomic relationship matrix from the SNP data and used it in a genomic best linear unbiased prediction (GBLUP) model. We assessed predictive ability as the correlation between predicted genetic values and observed phenotypes by cross-validation, and found a predictive ability of 0.239±0.008 (0.230±0.012) for starvation resistance (startle response). The predictive ability of BayesB, a Bayesian method with internal SNP selection, was not greater than GBLUP. Selection of the 5% SNPs with either the highest absolute effect or variance explained did not improve predictive ability. Predictive ability decreased only when fewer than 150,000 SNPs were used to construct the genomic relationship matrix. We hypothesize that predictive power in this population stems from the SNP–based modeling of the subtle relationship structure caused by long-range linkage disequilibrium and not from population structure or SNPs in linkage disequilibrium with causal variants. We discuss the implications of these results for genomic prediction in other organisms.

Highlights

Most efforts to understand the genetic architecture of quantitative traits have focused on mapping the variants causing phenotypic variation in quantitative trait locus (QTL) mapping populations derived from crosses between lines genetically divergent for the trait, or in association mapping populations, with the goal of understanding the biological underpinnings of trait variation [1]
We use,2.5 million single-nucleotide polymorphism (SNP) with minor allele frequency greater than 2.5% derived from genomic sequences of the ‘‘Drosophila Genetic Reference Panel’’ to predict phenotypes for two traits, starvation resistance and startle-induced locomotor behavior
We systematically address prediction within versus across sexes, genomic best linear unbiased prediction (GBLUP) versus a Bayesian approach, and the effect of SNP density

Summary

Introduction

Most efforts to understand the genetic architecture of quantitative traits have focused on mapping the variants causing phenotypic variation in quantitative trait locus (QTL) mapping populations derived from crosses between lines genetically divergent for the trait, or in association mapping populations, with the goal of understanding the biological underpinnings of trait variation [1]. The premise of personalized medicine is based on prediction of individual genetic risk to disease from genome-wide association studies [2,3], and the ability to select individuals or lines in animal and plant breeding programs based on genotypic information circumvents the costly process of progeny testing and reduces the generation interval in applied breeding programs, leading to greater efficiency [4,5]. The expected, pedigree-based numerator relationship matrix can be replaced by a realized, genome-based relationship matrix (often called the ‘‘genomic’’ relationship matrix, [8]). This approach is equivalent to a random regression approach in which all SNP genotypes are simulta-

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Discussion

Conclusion